rRNADepletion_StrippedSerum

Setup

library(tidyverse)
library(magrittr)
library(parallel)
library(ngsReports)
library(here)
library(scales)
library(ggpubr)
library(kableExtra)
library(AnnotationHub)
library(ensembldb)
library(edgeR)
library(corrplot)
library(DT)
library(ggrepel)
library(msigdbr)
library(fgsea)

if (interactive()) setwd(here::here())
theme_set(theme_bw())
cores <- detectCores() - 2

Sequence information

ah <- AnnotationHub() %>%
  subset(species == "Homo sapiens") %>%
  subset(rdataclass == "EnsDb")
ensDb <- ah[["AH83216"]]
trEns <- transcripts(ensDb) %>%
  mcols() %>% 
  as_tibble()
trLen <- exonsBy(ensDb, "tx") %>%
  width() %>%
  vapply(sum, integer(1))
geneGcLen <- trLen %>%
  enframe() %>%
  set_colnames(c("tx_id", "length")) %>%
  left_join(trEns) %>%
  group_by(gene_id) %>% 
  summarise(
    aveLen = mean(length),
    maxLen = max(length), 
    aveGc = sum(length * gc_content) / sum(length),
    longestGc = gc_content[which.max(length)[[1]]]
  ) %>%
  mutate(
    aveGc =  aveGc / 100,
    longestGc = longestGc / 100
  )
trGcLen <- trLen %>%
  enframe() %>%
  set_colnames(c("tx_id", "length")) %>%
  left_join(trEns) %>%
  group_by(tx_id) %>% 
  summarise(
    aveLen = mean(length),
    maxLen = max(length), 
    aveGc = sum(length * gc_content) / sum(length),
    longestGc = gc_content[which.max(length)[[1]]]
  ) %>%
  mutate(
    aveGc =  aveGc / 100,
    longestGc = longestGc / 100
  )
genesGR <- genes(ensDb)
mcols(genesGR) <- mcols(genesGR)[c("gene_id", "gene_name", 
                                   "gene_biotype", "entrezid")]
txGR <- transcripts(ensDb)
mcols(txGR) <- mcols(txGR)[c("tx_id", "tx_name", 
                             "tx_biotype", "tx_id_version", "gene_id")]

An EnsDb object was obtained for Ensembl release 101 using the AnnotationHub package. This provided the GC content and length for every gene and transcript in the release. For humans, this consists of 67990 genes and 252335 transcripts.

Raw data

Sample information

files <- list.files(
  path = "/hpcfs/users/a1647910/20200310_rRNADepletion/4_T47D_ZR75_DHT_StrippedSerum/0_rawData/FastQC",
  pattern = "zip",
  full.names = TRUE
)

samples <- tibble(
  sample = str_remove(basename(files), "_fastqc.zip"),
  dataset = NA,
  organism = NA
) %>%
  mutate(
    dataset = "StrippedSerum",
    organism = "human"
  )
datasets <- samples$dataset %>% 
  unique()

The following analysis involves 16 paired-end samples across 1 dataset(s): StrippedSerum.

Library sizes

rawFqc <- files %>%
  FastqcDataList()

data <- grep("GLL", fqName(rawFqc))
labels <- rawFqc[data] %>%
  fqName() %>%
  str_remove("_001\\.fastq\\.gz") %>%
  str_remove("Ps2Ex3M1_")
rawLib <- plotReadTotals(rawFqc[data]) +
  labs(subtitle = "StrippedSerum") + 
  scale_x_discrete(labels = labels)

The library sizes of the unprocessed dataset(s) range between 22,057,776 and 42,743,934 reads.

rawLib

GC content

rRNA transcripts are known to have high GC content. Therefore, inspecting the GC content of the raw reads is a logical start point for detecting incomplete rRNA removal. A spike in GC content at ~ 70% is expected if this is the case.

plotly::ggplotly(
  plotGcContent(
    x = rawFqc[data], 
    plotType = "line",
    gcType = "Transcriptome",
    species = "Hsapiens"
  ) +
    labs(title = "StrippedSerum Dataset (H. sapiens)") + 
    theme(legend.position="none")
) 

GC content of reads in the dataset. Clear spikes at about 70% GC are observed, which is likely due to incomplete rRNA depletion.

Overrepresented sequences

getModule(rawFqc, "Overrep") %>% 
  group_by(Sequence, Possible_Source) %>% 
  summarise(`Found In` = n(), `Highest Percentage` = max(Percentage)) %>% 
  arrange(desc(`Highest Percentage`), desc(`Found In`)) %>% 
  ungroup() %>% 
  dplyr::slice(1:30) %>%
  mutate(`Highest Percentage` = percent_format(0.01)(`Highest Percentage`/100)) %>%
  kable(
    align = "llrr", 
    caption = paste(
      "Top", nrow(.),"Overrepresented sequences.",
      "The number of samples they were found in is shown,",
      "along with the percentage of the most 'contaminated' sample."
    )
  ) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed", "responsive")
  )

Top 30 Overrepresented sequences. The number of samples they were found in is shown, along with the percentage of the most ‘contaminated’ sample.

Sequence	Possible_Source	Found In	Highest Percentage
CCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG	No Hit	16	0.84%
CTGGAGTCTTGGAAGCTTGACTACCCTACGTTCTCCTACAAATGGACCTT	No Hit	16	0.60%
CTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGA	No Hit	16	0.59%
CCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTAC	No Hit	8	0.55%
CCCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATAT	No Hit	16	0.50%
CCAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCA	No Hit	13	0.50%
CCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATT	No Hit	16	0.49%
CTTGGTTATAATTTTTCATCTTTCCCTTGCGGTACTATATCTATTGCGCC	No Hit	16	0.49%
CTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGGT	No Hit	16	0.47%
CTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGC	No Hit	15	0.45%
CCCTGTTCTTGGGTGGGTGTGGGTATAATGCTAAGTTGAGATGATATCAT	No Hit	8	0.41%
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA	No Hit	1	0.40%
CCCAAACCCACTCCACCCTACTACCAGACAACCTTAGCCAAACCATTTAC	No Hit	8	0.39%
CCCTGTTCTTGGGTGGGTGTGGGTATAATACTAAGTTGAGATGATATCAT	No Hit	8	0.39%
GTATAATACTAAGTTGAGATGATATCATTTACGGGGGAAGGCGCTTTGTG	No Hit	8	0.39%
CGGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTTCTCTTGTCCTTTCGTA	No Hit	16	0.36%
GTGGGTATAATGCTAAGTTGAGATGATATCATTTACGGGGGAAGGCGCTT	No Hit	8	0.33%
CTCTCTACAAGGTTTTTTCCTAGTGTCCAAAGAGCTGTTCCTCTTTGGAC	No Hit	16	0.33%
CTTATTTCTCTTGTCCTTTCGTACAGGGAGGAATTTGAAGTAGATAGAAA	No Hit	16	0.32%
GGGTATAATACTAAGTTGAGATGATATCATTTACGGGGGAAGGCGCTTTG	No Hit	8	0.31%
CTCAGACCGCGTTCTCTCCCTCTCACTCCCCAATACGGAGAGAAGAACGA	No Hit	12	0.31%
GTAAGATTTGCCGAGTTCCTTTTACTTTTTTTAACCTTTCCTTATGGGCA	No Hit	8	0.31%
CTGAACTCCTCACACCCAATTGGACCAATCTATCACCCTATAGAAGAACT	No Hit	16	0.29%
GTCCAATTGGGTGTGAGGAGTTCAGTTATATGTTTGGGATTTTTTAGGTA	No Hit	12	0.28%
CCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTG	No Hit	16	0.28%
CTGGTTTCGGGGGTCTTAGCTTTGGCTCTCCTTGCAAAGTTATTTCTAGT	No Hit	15	0.28%
CTCTAGAATAGGATTGCGCTGTTATCCCTAGGGTAACTTGTTCCGTTGGT	No Hit	16	0.26%
CTGTTCTTGGGTGGGTGTGGGTATAATACTAAGTTGAGATGATATCATTT	No Hit	5	0.25%
CTCCGAGGTCGCCCCAACCGAAATTTTTAATGCAGGTTTGGTAGTTTAGG	No Hit	16	0.25%
CTCAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATC	No Hit	13	0.25%

Trimmed data

Raw libraries were trimmed using cutadapt v1.14 to remove Illumina adapter sequences. Bases with PHRED score < 30, NextSeq-induced polyG runs and reads shorter than 35bp were also removed.

trimFqc <- list.files(
  path = "/hpcfs/users/a1647910/20200310_rRNADepletion/4_T47D_ZR75_DHT_StrippedSerum/1_trimmedData/FastQC",
  pattern = "zip",
  full.names = TRUE
) %>%
  FastqcDataList()
trimStats <- readTotals(rawFqc) %>%
  dplyr::rename(Raw = Total_Sequences) %>%
  left_join(readTotals(trimFqc), by = "Filename") %>%
  dplyr::rename(Trimmed = Total_Sequences) %>%
  mutate(
    Discarded = 1 - Trimmed/Raw,
    Retained = Trimmed / Raw
  )

After trimming of adapters between 6.09% and 8.36% of reads were discarded.

Aligned data

Trimmed reads were:

1. Aligned to rRNA sequences using the BWA-MEM algorithm to estimate the proportion of reads that were of rRNA origin within each sample. BWA-MEM is recommended for high-quality queries of reads ranging from 70bp to 1Mbp as it is faster and more accurate that alternative algorithms BWA-backtrack and BWA-SW.

2. Aligned to the Homo sapiens GRCh38 genome (Ensembl release 101) using STAR v2.7.0d and summarised with featureCounts from the Subread v1.5.2 package. These counts were used for all gene-level analysis.

rRNA proportions

rRnaProp <- read.delim(
  "/hpcfs/users/a1647910/20200310_rRNADepletion/4_T47D_ZR75_DHT_StrippedSerum/3_bwa/log/samples.mapped.all", 
  sep = ":", 
  col.names = c("sample", "proportion"), 
  header = FALSE
) %>% 
  mutate(
    sample = str_remove_all(sample, ".mapped"),
    sample = basename(sample),
    proportion = proportion/100,
    dataset = "StrippedSerum",
    organism = "human"
  ) %>%
  as_tibble()
rRnaProp$dataset %<>%
  factor(levels = c("StrippedSerum"))

rRnaProp %>%
  ggplot(aes(sample, proportion)) +
  geom_bar(stat = "identity", position = "dodge") +
  facet_wrap(~dataset, scales = "free_x") +
  scale_y_continuous(labels = percent) +
  labs(x = "Sample", y = "Percent of Total", fill = "Read pair") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Percentages of each library that align to rRNA sequences with bwa mem.

Gene GC content and length

dgeList <- read_tsv("/hpcfs/users/a1647910/20200310_rRNADepletion/4_T47D_ZR75_DHT_StrippedSerum/4_star2pass/featureCounts/genes.out") %>%
  set_colnames(basename(colnames(.))) %>%
  set_colnames(str_remove(colnames(.), "Aligned.sortedByCoord.out.bam")) %>%
  as.data.frame() %>%
  column_to_rownames("Geneid") %>%
  DGEList() %>%
  calcNormFactors()
metaData <- read_tsv("/hpcfs/users/a1647910/20200310_rRNADepletion/4_T47D_ZR75_DHT_StrippedSerum/T47D_ZR75_DHT_StrippedSerum.tsv")

dgeList$genes <- genesGR[rownames(dgeList),]
mcols(dgeList$genes) %<>% 
  as.data.frame() %>% 
  left_join(geneGcLen)
addInfo <- tibble(
  sample = rRnaProp$sample,
  dataset = "StrippedSerum",
  organism = "human",
  rRNA = rRnaProp$proportion
) %>%
  left_join(metaData)
dgeList$samples %<>%
  rownames_to_column("rowname") %>%
  mutate(sample = rowname) %>%
  left_join(addInfo) %>%
  column_to_rownames("rowname") %>%
  mutate(
    filenames = paste0(
      "/hpcfs/users/a1647910/20200310_rRNADepletion/",
      "4_T47D_ZR75_DHT_StrippedSerum/4_star2pass/bam/",
      sample,
      "Aligned.sortedByCoord.out.bam"
    )
  )
dgeList$samples$filenames <- list.files(
  "/hpcfs/users/a1647910/20200310_rRNADepletion/4_T47D_ZR75_DHT_StrippedSerum/4_star2pass/bam", 
  pattern = ".bam$", 
  full.names = TRUE
)
dgeList$samples$group <- paste0(
  dgeList$samples$cell_line, "_", dgeList$samples$treat
) %>%
  make.names() %>%
  factor(levels = unique(.))

gcInfo <- function(x) {
  x$counts %>%
    as.data.frame() %>%
    rownames_to_column("gene_id") %>%
    as_tibble() %>%
    pivot_longer(
      cols = colnames(x), 
      names_to = "sample", 
      values_to = "counts"
    ) %>%
    dplyr::filter(
      counts > 0
    ) %>%
    left_join(
      geneGcLen
    ) %>%
    dplyr::select(
      ends_with("id"), sample, counts, aveGc, maxLen
    ) %>%
    split(f = .$sample) %>%
    lapply(
      function(x){
        DataFrame(
          gc = Rle(x$aveGc, x$counts),
          logLen = Rle(log10(x$maxLen), x$counts)
        )
      }
    ) 
}
gcSummary <- function(x) {
  x %>%
    vapply(function(x){
      c(mean(x$gc), sd(x$gc), mean(x$logLen), sd(x$logLen))
    }, numeric(4)
    ) %>%
    t() %>%
    set_colnames(
      c("mn_gc", "sd_gc", "mn_logLen", "sd_logLen")
    ) %>%
    as.data.frame() %>%
    rownames_to_column("sample") %>%
    as_tibble()
}

rle <- gcInfo(dgeList)

sumGc <- gcSummary(rle)

a <- sumGc %>%
  left_join(dgeList$samples) %>%
  ggplot(aes(rRNA, mn_logLen)) +
  geom_point(aes(colour = treat, shape = cell_line), size = 3) +
  geom_smooth(method = "lm") +
  scale_x_continuous(labels = percent) +
  labs(
    x = "rRNA Proportion of Initial Library",
    y = "Mean log(Length)",
    colour = "Treatment",
    shape = "Cell line"
  ) 
b <- sumGc %>%
  left_join(dgeList$samples) %>%
  ggplot(aes(rRNA, mn_gc)) +
  geom_point(aes(colour = treat, shape = cell_line), size = 3) +
  geom_smooth(method = "lm") +
  scale_y_continuous(labels = percent) +
  scale_x_continuous(labels = percent) +
  labs(
    x = "rRNA Proportion of Initial Library",
    y = "Mean GC Content",
    colour = "Treatment",
    shape = "Cell line"
  )
ggarrange(
  a, b, ncol = 2, nrow = 1, 
  common.legend = TRUE, legend = "bottom"
) %>%
  annotate_figure("StrippedSerum Dataset (H. sapiens)")

Comparison of residual bias potentially introduced by incomplete rRNA removal. Regression lines are shown along with standard error bands for each comparison.

PCA

genes2keep <- dgeList %>%
  cpm() %>%
  is_greater_than(1) %>%
  rowSums() %>%
  is_weakly_greater_than(6)
dgeFilt <- dgeList[genes2keep,, keep.lib.sizes = FALSE] %>%
  calcNormFactors()

pca <- cpm(dgeFilt, log = TRUE) %>%
  t() %>%
  prcomp()

pcaCor <- pca$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sumGc) %>%
  as_tibble() %>% 
  left_join(dgeList$samples) %>%
  dplyr::select(
    PC1, PC2, PC3, 
    Mean_GC = mn_gc, 
    Mean_Length = mn_logLen, 
    rRna_Proportion = rRNA
  ) %>% 
  cor()

a <- pca$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(dgeList$samples) %>%
  as_tibble() %>%
  ggplot(aes(PC1, PC2)) +
  geom_point(aes(colour = treat, shape = cell_line), size = 2) +
  labs(
    x = paste0("PC1 (", percent(summary(pca)$importance["Proportion of Variance","PC1"]),")"),
    y = paste0("PC2 (", percent(summary(pca)$importance["Proportion of Variance","PC2"]),")"),
    colour = "Treatment",
    shape = "Cell line"
  )
b <- pca$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(dgeList$samples) %>%
  ggplot(aes(PC1, rRNA, label = rRNA)) +
  geom_point(aes(colour = treat, shape = cell_line), size = 2) +
  geom_smooth(method = "lm") +
  geom_text_repel(show.legend = FALSE) +
  scale_y_continuous(labels = percent) +
  labs(
    x = paste0("PC1 (", percent(summary(pca)$importance["Proportion of Variance","PC1"]),")"),
    y = "rRNA Proportion of Initial Library",
    colour = "Treatment",
    shape = "Cell line"
  )
c <- pca$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sumGc) %>%
  left_join(dgeList$samples) %>%
  as_tibble() %>%
  ggplot(aes(PC1, mn_logLen)) +
  geom_point(aes(colour = treat, shape = cell_line), size = 2) +
  geom_smooth(method = "lm") +
  labs(
    x = paste0("PC1 (", percent(summary(pca)$importance["Proportion of Variance","PC1"]),")"),
    y = "Mean log(Length)",
    colour = "Treatment",
    shape = "Cell line"
  )
d <- pca$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sumGc) %>%
  left_join(dgeList$samples) %>%
  as_tibble() %>%
  ggplot(aes(PC1, mn_gc)) +
  geom_point(aes(colour = treat, shape = cell_line), size = 2) +
  geom_smooth(method = "lm") +
  scale_y_continuous(labels = percent) +
  labs(
    x = paste0("PC1 (", percent(summary(pca)$importance["Proportion of Variance","PC1"]),")"),
    y = "Mean GC",
    colour = "Treatment",
    shape = "Cell line"
  )
ggarrange(
  a, b, c, d, ncol = 2, nrow = 2,
  common.legend = TRUE, legend = "bottom"
) %>%
  annotate_figure("StrippedSerum")

PCA plot showing rRNA proportion, mean GC content and mean log(length) after summarisation to gene-level.

corrplot(
  pcaCor,
  type = "lower", 
  diag = FALSE, 
  addCoef.col = 1, addCoefasPercent = TRUE
)

Correlations between the first three principal components and measured variables: mean GC content, mean log(length) and rRNA proportion.

Differential expression

displayRes_de <- function(x){
  de <- x %>%
    dplyr::filter(DE)
  de %>%
    dplyr::slice(1:1000) %>%
    dplyr::select(-gene_biotype, -coef, -DE) %>%
    mutate(across(c("P.Value", "FDR", "Bonf"), ~ sprintf("%.2e", .x))) %>%
    datatable(
      options = list(pageLength = 10), 
      class = "striped hover condensed responsive", 
      filter = "top",
      caption = paste0(
        x$coef[1],
        ": ",
        nrow(de),
        " of ",
        nrow(x),
        " genes were classified as differentially expressed ",
        "with a FDR < 0.05. ",
        "If more than 1000 genes were classified as DE, only the top 1000 are shown."
      )
    ) %>%
    formatRound(c("logFC", "logCPM", "F"), digits = 2)
}

rRNA

design_r <- model.matrix(~rRNA, data = dgeFilt$samples)

fit_r <- dgeFilt %>%
  estimateDisp(design = design_r) %>%
  glmQLFit() 

res_r <- glmQLFTest(fit_r) %>%
  topTags(n = Inf) %>%
  .[["table"]] %>%
  rename_all(
    str_remove, pattern = "ID."
  ) %>%
  dplyr::select(
    Geneid = gene_id, Symbol = gene_name, gene_biotype, logFC, logCPM, F, 
    P.Value = PValue, FDR, aveLen, maxLen, aveGc, longestGc
  ) %>%
  as_tibble() %>%
  mutate(
    Bonf = p.adjust(P.Value, "bonf"),
    DE = FDR < 0.05
  )

res_r %>%
  dplyr::filter(DE) %>%
  dplyr::slice(1:1000) %>%
  dplyr::select(-gene_biotype, -DE, -aveLen, -maxLen, -aveGc, -longestGc) %>%
  mutate(across(c("P.Value", "FDR", "Bonf"), ~ sprintf("%.2e", .x))) %>%
  datatable(
    options = list(pageLength = 10), 
    class = "striped hover condensed responsive", 
    filter = "top",
    caption = paste0(
      "rRNA: ",
      nrow(dplyr::filter(res_r, DE)),
      " of ",
      nrow(res_r),
      " genes were classified as differentially expressed ",
      "with a FDR < 0.05. ",
      "If more than 1000 genes were classified as DE, only the top 1000 are shown."
    )
  ) %>%
  formatRound(c("logFC", "logCPM", "F"), digits = 2)

Treatment

design_t <- model.matrix(~0+group, data = dgeFilt$samples)
cont_t <- makeContrasts(
  groupT.47D_DHT = groupT.47D_DHT - groupT.47D_Vehicle,
  groupZR.75.1_DHT = groupZR.75.1_DHT - groupZR.75.1_Vehicle,
  levels = colnames(design_t)
)

fit_t <- dgeFilt %>%
  estimateDisp(design = design_t) %>%
  glmQLFit() 

res_t <- colnames(cont_t) %>%
  sapply(function(x){
    glmQLFTest(fit_t, contrast = cont_t[,x]) %>%
      topTags(n = Inf) %>%
      .[["table"]] %>%
      rename_all(
        str_remove, pattern = "ID."
      ) %>%
      dplyr::select(
        Geneid = gene_id, Symbol = gene_name, gene_biotype, logFC, logCPM, F, 
        P.Value = PValue, FDR,
      ) %>%
      as_tibble() %>%
      mutate(
        Bonf = p.adjust(P.Value, "bonf"), 
        coef = x,
        DE = FDR < 0.05
      )
  },
  simplify = FALSE)

displayRes_de(res_t$groupT.47D_DHT)

displayRes_de(res_t$groupZR.75.1_DHT)

rRNA + treatment

design_tr <- model.matrix(~0+rRNA+group, data = dgeFilt$samples)
cont_tr <- makeContrasts(
  groupT.47D_DHT = groupT.47D_DHT - groupT.47D_Vehicle,
  groupZR.75.1_DHT = groupZR.75.1_DHT - groupZR.75.1_Vehicle,
  rRNA = rRNA,
  levels = colnames(design_tr)
)

fit_tr <- dgeFilt %>%
  estimateDisp(design = design_tr) %>%
  glmQLFit() 

res_tr <- colnames(cont_tr) %>%
  sapply(function(x){
    glmQLFTest(fit_tr, contrast = cont_tr[,x]) %>%
      topTags(n = Inf) %>%
      .[["table"]] %>%
      rename_all(
        str_remove, pattern = "ID."
      ) %>%
      dplyr::select(
        Geneid = gene_id, Symbol = gene_name, gene_biotype, logFC, logCPM, F, 
        P.Value = PValue, FDR,
      ) %>%
      as_tibble() %>%
      mutate(
        Bonf = p.adjust(P.Value, "bonf"), 
        coef = x,
        DE = FDR < 0.05
      )
  },
  simplify = FALSE)

displayRes_de(res_tr$rRNA)

displayRes_de(res_tr$groupT.47D_DHT)

displayRes_de(res_tr$groupZR.75.1_DHT)

Enrichment

entrezGenes <- mcols(dgeList$genes) %>%
  as.data.frame() %>%
  dplyr::filter(!is.na(entrezid)) %>%
  unnest(cols = entrezid) %>%
  dplyr::rename(entrez_gene = entrezid)

ranks_r <- res_r %>%
      mutate(stat = -sign(logFC) * log10(P.Value)) %>%
      dplyr::arrange(stat) %>%
      with(structure(stat, names = Geneid))

ranks_t <- res_t %>%
  lapply(function(x){
    x %>%
      mutate(stat = -sign(logFC) * log10(P.Value)) %>%
      dplyr::arrange(stat) %>%
      with(structure(stat, names = Geneid))
  })

ranks_tr <- res_tr %>%
  lapply(function(x){
    x %>%
      mutate(stat = -sign(logFC) * log10(P.Value)) %>%
      dplyr::arrange(stat) %>%
      with(structure(stat, names = Geneid))
  })

displayRes_enrich <- function(x, cap){
  x %>%
    unnest(cols = leadingEdge) %>%
    group_by(pathway) %>%
    mutate(
      leadingSize = n(),
      pathway = str_remove(pathway, "HALLMARK_|KEGG_|WP_"),
      pathway = str_trunc(pathway, 33)
    ) %>%
    distinct(pathway, .keep_all = TRUE) %>%
    dplyr::select(-leadingEdge) %>%
    mutate(across(c("pval", "FDR", "padj"), ~ sprintf("%.2e", .x))) %>%
    datatable(
      options = list(pageLength = 10), 
      class = "striped hover condensed responsive", 
      filter = "top",
      caption = paste(cap)
    ) %>%
    formatRound(c("log2err", "ES", "NES"), digits = 2)
}

Databases

hm <- msigdbr("Homo sapiens", category = "H")  %>% 
  left_join(entrezGenes) %>%
  dplyr::filter(!is.na(gene_id)) %>%
  distinct(gs_name, gene_id, .keep_all = TRUE)
hmByGene <- hm %>%
  split(f = .$gene_id) %>%
  lapply(extract2, "gs_name")
hmByID <- hm %>%
  split(f = .$gs_name) %>%
  lapply(extract2, "gene_id")

kg <- msigdbr("Homo sapiens", category = "C2", subcategory = "CP:KEGG")  %>% 
  left_join(entrezGenes) %>%
  dplyr::filter(!is.na(gene_id)) %>%
  distinct(gs_name, gene_id, .keep_all = TRUE)
kgByGene <- kg  %>%
  split(f = .$gene_id) %>%
  lapply(extract2, "gs_name")
kgByID <- kg  %>%
  split(f = .$gs_name) %>%
  lapply(extract2, "gene_id")

wk <- msigdbr("Homo sapiens", category = "C2", subcategory = "CP:WIKIPATHWAYS")  %>% 
  left_join(entrezGenes) %>%
  dplyr::filter(!is.na(gene_id)) %>%
  distinct(gs_name, gene_id, .keep_all = TRUE)
wkByGene <- wk  %>%
  split(f = .$gene_id) %>%
  lapply(extract2, "gs_name")
wkByID <- wk  %>%
  split(f = .$gs_name) %>%
  lapply(extract2, "gene_id")

gsSizes <- bind_rows(hm, kg, wk) %>% 
  dplyr::select(gs_name, gene_symbol, gene_id) %>% 
  chop(c(gene_symbol, gene_id)) %>%
  mutate(
    gs_size = vapply(gene_symbol, length, integer(1))
  )

Hallmark

rRNA

fgsea_r_hm <-fgsea(hmByID, ranks_r, eps = 0) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)

displayRes_enrich(fgsea_r_hm, "rRNA")

Treatment

fgsea_t_hm <- ranks_t %>%
  lapply(function(x){
    fgsea(hmByID, x, eps = 0) %>%
      as_tibble() %>%
      dplyr::rename(FDR = padj) %>%
      mutate(padj = p.adjust(pval, "bonferroni")) %>%
      dplyr::arrange(pval)
  })

displayRes_enrich(fgsea_t_hm$groupT.47D_DHT, "T-47D_DHT")

displayRes_enrich(fgsea_t_hm$groupZR.75.1_DHT, "ZR-75-1_DHT")

rRNA + treatment

fgsea_tr_hm <- ranks_tr %>%
  lapply(function(x){
    fgsea(hmByID, x, eps = 0) %>%
      as_tibble() %>%
      dplyr::rename(FDR = padj) %>%
      mutate(padj = p.adjust(pval, "bonferroni")) %>%
      dplyr::arrange(pval)
  })

displayRes_enrich(fgsea_tr_hm$rRNA, "rRNA")

displayRes_enrich(fgsea_tr_hm$groupT.47D_DHT, "T-47D_DHT")

displayRes_enrich(fgsea_tr_hm$groupZR.75.1_DHT, "ZR-75-1_DHT")

KEGG

rRNA

fgsea_r_kg <-fgsea(kgByID, ranks_r, eps = 0) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)

displayRes_enrich(fgsea_r_kg, "rRNA")

Treatment

fgsea_t_kg <- ranks_t %>%
  lapply(function(x){
    fgsea(kgByID, x, eps = 0) %>%
      as_tibble() %>%
      dplyr::rename(FDR = padj) %>%
      mutate(padj = p.adjust(pval, "bonferroni")) %>%
      dplyr::arrange(pval)
  })

displayRes_enrich(fgsea_t_kg$groupT.47D_DHT, "T-47D_DHT")

displayRes_enrich(fgsea_t_kg$groupZR.75.1_DHT, "ZR-75-1_DHT")

rRNA + treatment

fgsea_tr_kg <- ranks_tr %>%
  lapply(function(x){
    fgsea(kgByID, x, eps = 0) %>%
      as_tibble() %>%
      dplyr::rename(FDR = padj) %>%
      mutate(padj = p.adjust(pval, "bonferroni")) %>%
      dplyr::arrange(pval)
  })

displayRes_enrich(fgsea_tr_kg$rRNA, "rRNA")

displayRes_enrich(fgsea_tr_kg$groupT.47D_DHT, "T-47D_DHT")

displayRes_enrich(fgsea_tr_kg$groupZR.75.1_DHT, "ZR-75-1_DHT")

Wikipathways

rRNA

fgsea_r_wk <-fgsea(wkByID, ranks_r, eps = 0) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)

displayRes_enrich(fgsea_r_wk, "rRNA")

Treatment

fgsea_t_wk <- ranks_t %>%
  lapply(function(x){
    fgsea(wkByID, x, eps = 0) %>%
      as_tibble() %>%
      dplyr::rename(FDR = padj) %>%
      mutate(padj = p.adjust(pval, "bonferroni")) %>%
      dplyr::arrange(pval)
  })

displayRes_enrich(fgsea_t_wk$groupT.47D_DHT, "T-47D_DHT")

displayRes_enrich(fgsea_t_wk$groupZR.75.1_DHT, "ZR-75-1_DHT")

rRNA + treatment

fgsea_tr_wk <- ranks_tr %>%
  lapply(function(x){
    fgsea(wkByID, x, eps = 0) %>%
      as_tibble() %>%
      dplyr::rename(FDR = padj) %>%
      mutate(padj = p.adjust(pval, "bonferroni")) %>%
      dplyr::arrange(pval)
  })

displayRes_enrich(fgsea_tr_wk$rRNA, "rRNA")

displayRes_enrich(fgsea_tr_wk$groupT.47D_DHT, "T-47D_DHT")

displayRes_enrich(fgsea_tr_wk$groupZR.75.1_DHT, "ZR-75-1_DHT")

Session info

save(
  addInfo,
  dgeList,
  dgeFilt,
  res_r,
  res_t,
  res_tr,
  file = here::here(
    "4_T47D_ZR75_DHT_StrippedSerum/R/output/4_1_DE.RData"
  )
)

sessionInfo()

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
##  [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
##  [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] fgsea_1.14.0            msigdbr_7.2.1           ggrepel_0.8.2          
##  [4] DT_0.14                 corrplot_0.84           edgeR_3.30.3           
##  [7] limma_3.44.3            ensembldb_2.12.1        AnnotationFilter_1.12.0
## [10] GenomicFeatures_1.40.1  AnnotationDbi_1.50.1    Biobase_2.48.0         
## [13] GenomicRanges_1.40.0    GenomeInfoDb_1.24.2     IRanges_2.22.2         
## [16] S4Vectors_0.26.1        AnnotationHub_2.20.0    BiocFileCache_1.12.0   
## [19] dbplyr_1.4.4            kableExtra_1.1.0        ggpubr_0.4.0           
## [22] scales_1.1.1            here_0.1                ngsReports_1.4.2       
## [25] BiocGenerics_0.34.0     magrittr_1.5            forcats_0.5.0          
## [28] stringr_1.4.0           dplyr_1.0.0             purrr_0.3.4            
## [31] readr_1.3.1             tidyr_1.1.0             tibble_3.0.3           
## [34] ggplot2_3.3.2           tidyverse_1.3.0        
## 
## loaded via a namespace (and not attached):
##   [1] tidyselect_1.1.0              RSQLite_2.2.0                
##   [3] htmlwidgets_1.5.1             FactoMineR_2.3               
##   [5] grid_4.0.3                    BiocParallel_1.22.0          
##   [7] munsell_0.5.0                 withr_2.2.0                  
##   [9] colorspace_1.4-1              highr_0.8                    
##  [11] knitr_1.29                    rstudioapi_0.11              
##  [13] leaps_3.1                     ggsignif_0.6.0               
##  [15] labeling_0.3                  GenomeInfoDbData_1.2.3       
##  [17] hwriter_1.3.2                 bit64_0.9-7.1                
##  [19] farver_2.0.3                  rprojroot_1.3-2              
##  [21] vctrs_0.3.2                   generics_0.0.2               
##  [23] xfun_0.15                     R6_2.4.1                     
##  [25] locfit_1.5-9.4                bitops_1.0-6                 
##  [27] DelayedArray_0.14.1           assertthat_0.2.1             
##  [29] promises_1.1.1                gtable_0.3.0                 
##  [31] Cairo_1.5-12.2                rlang_0.4.7                  
##  [33] scatterplot3d_0.3-41          splines_4.0.3                
##  [35] rtracklayer_1.48.0            rstatix_0.6.0                
##  [37] lazyeval_0.2.2                broom_0.7.0                  
##  [39] BiocManager_1.30.10           yaml_2.2.1                   
##  [41] reshape2_1.4.4                abind_1.4-5                  
##  [43] modelr_0.1.8                  crosstalk_1.1.0.1            
##  [45] backports_1.1.8               httpuv_1.5.4                 
##  [47] tools_4.0.3                   ellipsis_0.3.1               
##  [49] RColorBrewer_1.1-2            ggdendro_0.1.22              
##  [51] Rcpp_1.0.5                    plyr_1.8.6                   
##  [53] progress_1.2.2                zlibbioc_1.34.0              
##  [55] RCurl_1.98-1.2                prettyunits_1.1.1            
##  [57] openssl_1.4.2                 cowplot_1.0.0                
##  [59] zoo_1.8-8                     SummarizedExperiment_1.18.2  
##  [61] haven_2.3.1                   cluster_2.1.0                
##  [63] fs_1.4.2                      data.table_1.12.8            
##  [65] openxlsx_4.1.5                reprex_0.3.0                 
##  [67] truncnorm_1.0-8               ProtGenerics_1.20.0          
##  [69] matrixStats_0.56.0            hms_0.5.3                    
##  [71] mime_0.9                      evaluate_0.14                
##  [73] xtable_1.8-4                  XML_3.99-0.4                 
##  [75] rio_0.5.16                    jpeg_0.1-8.1                 
##  [77] readxl_1.3.1                  gridExtra_2.3                
##  [79] compiler_4.0.3                biomaRt_2.44.1               
##  [81] crayon_1.3.4                  htmltools_0.5.0              
##  [83] mgcv_1.8-33                   later_1.1.0.1                
##  [85] lubridate_1.7.9               DBI_1.1.0                    
##  [87] MASS_7.3-53                   rappdirs_0.3.1               
##  [89] ShortRead_1.46.0              Matrix_1.2-18                
##  [91] car_3.0-8                     cli_2.0.2                    
##  [93] pkgconfig_2.0.3               flashClust_1.01-2            
##  [95] GenomicAlignments_1.24.0      foreign_0.8-80               
##  [97] plotly_4.9.2.1                xml2_1.3.2                   
##  [99] webshot_0.5.2                 XVector_0.28.0               
## [101] rvest_0.3.5                   digest_0.6.25                
## [103] Biostrings_2.56.0             rmarkdown_2.3                
## [105] cellranger_1.1.0              fastmatch_1.1-0              
## [107] curl_4.3                      shiny_1.5.0                  
## [109] Rsamtools_2.4.0               lifecycle_0.2.0              
## [111] nlme_3.1-149                  jsonlite_1.7.0               
## [113] carData_3.0-4                 viridisLite_0.3.0            
## [115] askpass_1.1                   fansi_0.4.1                  
## [117] pillar_1.4.6                  lattice_0.20-41              
## [119] fastmap_1.0.1                 httr_1.4.1                   
## [121] interactiveDisplayBase_1.26.3 glue_1.4.1                   
## [123] zip_2.0.4                     png_0.1-7                    
## [125] pander_0.6.3                  BiocVersion_3.11.1           
## [127] bit_1.1-15.2                  stringi_1.4.6                
## [129] blob_1.2.1                    latticeExtra_0.6-29          
## [131] memoise_1.1.0